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ABSTRACT

The debate on the relationship between life expectancy and population growth rate has been undergoing and varies across countries. This study provided a non parametric inference of the relationship between life expectancy and population growth rate on historical data for about 194 countries of the world reported in 2013. The first theory stated that population growth rate does not stimulate life expectancy. The second theory viewed population growth rate as a factor that adversely affects the life expectancy. The study employed the Statistical Package for Social Sciences (SPSS 19) to establish and identify the countries of the world that fall below the world 70.01 years standard. Hence, summary, conclusion and recommendations were given to the government and the entire public based on the findings towards for further study.

1. INTRODUCTION

Life Expectancy is a statistical measure of how long individuals or organisms may live, based on the year of their birth, their current age and other demographic factors including gender. At a given age, life expectancy is the average number of year that is likely to be lived by group of individuals (of age x) exposed to the same mortality conditions until they die. The most commonly used measure of life expectancy is life expectancy at age zero, that is, Live Expectancy at Birth (LEB), which can be defined in two ways: Cohort Life Expectancy at Birth and Period Life Expectancy at Birth. Cohort LEB is the mean length of life of an actual birth cohort (all individual born in a given year) and can be computed only for cohorts that were born many decades ago, so that all their members died. However, Period LEB is the mean length of life of hypothetical cohort assumed to be exposed since birth until death of all their members to the mortality rate observed at a given year.

Bhargava (2003) uses a parametric panel data specification and found that the dynamics of demography indicators such as lagged life expectancy variable is a significant predictor of economic growth. Charkraborty and Idrani (2010) develops a theoretical model and checked its empirical consistency using a parametric cross-country regression. The author found that life expectancy has a strong and positive effect on capital accumulation.

The rate of growth of the African population since the middle of the century, compared to the rest of World is both alarming and distressing; especially when taken in the context of the deteriorating quality of life expectancy of ordinary people. It was observed, for instance that in 1953, Nigeria’s population as one of the African countries was put at 31 million, and ten years later, the officially accepted estimated figure was 56 million. In 1985, the estimated figure was 98 million. Nigerian population drew from 91 million in 1991 to 160 million in 2006 and it is estimated to be 173 million in 2012. The increase is 90 percent for the period 1991 to 2012. Presently, the population estimated figure is also put around 179 million. It means that within 21 years, Nigeria population increased by 79% (CIA World Fact Books, 2011).

Although, several factors have been identified on the propelling variables, the needed condition for such excessive population growth must be looked for in several perspectives. While, some school of thoughts have considered the relationship between population growth and economic development among other social environment and political indicators, there are no known literature that has expressly anchored the relationship between life expectancy and population growth rate, which is the major issue of investigation in this study.

1.1. Statement of the Problem

There is continued divergence of opinions regarding the consequences of life expectancy and population growth. The debate between positive impact and negative impact of population growth rate on the life expectancy is still ongoing. On the positive side, population growth induces technological advancements and innovations. This is because population growth encourages competition in business activities and, as the country’s population grows, the size of its potential market expands as well. The expansion of the market, in its turns, encourages entrepreneurs to set up new businesses (Simon, 1992).

A large population growth on the other side is not only associated with food problem but also imposes constraints on the development of savings, foreign exchange and human resources. The increase in demand for food leads to a decrease in natural resources, which are needed for a nation to survive. Other negative effects of population growth include poverty caused by low income per capita, famine, and disease since rapid population growth complicates the task of providing and maintaining the infrastructure, education and health care needed in modern economies, which reduce the life expectancy (Barro, 1991); (Mankiw et al., 1992). Thus, this study intends to make a significant contribution to the study of relationship between life expectancy and population growth rate on a general note.

1.2. Aim

The aim of this study is to predict the relationship between life expectancy and population growth rate. Hence, the specific objectives are:

to determine the relationship between life expectancy and population growth rate;

to predict the relationship between life expectancy and population growth rate across countries based on certain classification;

to postulate a law relating the life expectancy and population growth rate; and

to identify policy implications from the study.

1.3. Literature Review

1.3.1. Introduction

The literature review focuses on both general and empirical studies carried out to examine the relationship between life expectancy and population growth rate.

2. GENERAL LITERATURE

Malthus (1998) believes that the world's population tends to increase at a faster rate than its food supply whereas, population grows at a geometric rate, and production capacity only grows arithmetically. Therefore, in the absence of consistent checks on life expectancy and population growth, Malthus made the prediction that in a short period of time, scarce resources will have to be shared among an increasing number of individuals. However, such checks that ease the pressure of population explosion do exist, and Malthus distinguished between two categories: the preventive check and the positive one. The preventive check consists of voluntary limitations of life expectancy and population growth. Individuals before getting married and building a family, make rational decisions based on the income they expect to earn and the quality of life they anticipate to maintain in the future for themselves and their families. The positive check to population is a direct consequence of the lack of a preventive check. When society does not limit population growth voluntarily; diseases, famines and wars reduce population size and establish the necessary balance with resources. In traditional African society, the wealth of an individual was accessed by the share size of his household. The household may include several wives, numerous children, many relatives as well as a significant number of labourers. Moreover, these activities of man therefore tend to reduce life expectancy.

However, this household together contributed his pool of labour for farming and other productive purposes. Another index of a man’s wealth and status is the size of his herds of cattle, sheep and goats. Essentially then, the household is, in the past, the pivotal basis for assessing a man’s social relevance and importance in the society.

Simplicity of this setting was further accentuated because the traditional African society either little or no financial cost of the now basic concerns of social existence such as education, housing, food, transport, health and similar infrastructural necessities which form the nexus of modern developmental activities. However, the population density was low, the life style of people was simple and the individual and society were equilibrium with each other.

Overtime, and especially with colonialism, the situation changed and Nigeria and indeed most African countries entered a new period where the emphasis of social existence became anchored on the modernization process and a modern science. This led to a sharp reduction infant, and a significant rise in life expectancy.

Traditional social arrangements, however, continued to favour polygamy on the basis of family formation and to indicate both tacit and explicit preference for large family sizes. In fact, a large family was seen as a form of social security and a safety value against the deleterious effect of high infant and material mortality and short life expectancy.

In the same manner, the barrenness of women was more often than not linked to heinous or diabolic influence within the household or society. This was in essence a high degree of social obsession with issues of fertility and the survival of the lineage.

There was in addition, a preference for and pre-occupation to have, male children. The number of children, especially male children that a woman had, in fact, came to determine, to some extent, her standing and importance within the extended family.

In recent times, the situation has been further aggravated by certain religious and social beliefs that frown at or discourage modern contraceptives and abortion. This added to the effects of universality of conjugal relations, high illiteracy, social inequality suffered by women and the subsistence mode of production which defined and allotted social, economic and political roles to different individuals in the society.

However, a large population cannot be said to be entirely bad or undesirable. There is the widely persuasive preposition of the pro-population school that high population density is pre-requisite for technological advancement and economic development (World Health Organisation, 2004). Besides, in conventional economic terms, it has been argued that a large population meant a bigger market, a greater volume of production, higher productivity, smaller transport distance and a greater diversity of ideas for societal growth and development. The conflict between the pro-population and the anti-population schools highlighted the complication of the conflicts arising from the difficulties of establishing any correlation between population growth and economic development in African countries especially on the basis of such parameter as per capital national income and other economic indicators. The first consequence has to do with the deteriorating effects on the general development of the state. The growth in population tends to encourage migration to urban centres. Given the low level of our urbanization process, such massive migrations, as are now being witnessed in the continent, put a severe strain on the limited urban infrastructure and facilities through over-utilization, thereby giving rise to great inadequacy and frequent breakdowns.

These are also compounded by over-crowding, environmental population and degradation and increased anti-social behaviours, all of which lead to the deterioration of the standard of living and quality of life that frequently defy official solution, which however reduce life expectancy in the part of the community.

The proliferation of informal economic activities to help migrants find some gainful employment aggravates the level of environmental population. The nations or continent capacity to cope effectively with these problems become important. African population is comparatively young and non-working. Those within 0 – 15 years age bracket constitute about half or more precisely 47% of these population, while those aged sixty four years above account for about 02%. The consequence is that every productive Africa is unwillingly saddled with the responsibility of feeding, housing, clothing and educating a child. This is in comparison to the situation in some developed countries where on the average two or three economically productive person provide for only one non – productive citizen. The irony of the situation is deemed obvious given the low level of incomes and miserably low level of investment in developing countries. However, this can also reduce life expectancy. It is also observed that in Nigeria as is the case with most developing countries, the practice of having large families was more prevalent among the poor than among the rich. This practice certainty constitutes a strong strain on resource and poses a real threat to the security which the extended family system offers.

Rapid population growth are multifarious and multi dimensional. The implication for two productive, for example, Nigeria would have to double the existing two supplies and significantly explain is infrastructure, utilities and service within the next twenty years just to maintain the present per capital standard and quality of life because of the increased demand generated by the burgeoning population. For instance, the United Nations Fund for Population Activities (UNFPA) population card, Nigeria’s population today is projected to increase by about 11 persons per minute. This means an additional 660 hungry mouths to be fed every hour. Given the present estimated growth rate of 3 – 3.4% a year, the population of Nigeria is expected to double by the year 2020 to about 250 million. This is in spite of the unacceptable high infant mortality rate of 144 per thousand per year, a high maternal mortality rate of about 20 per thousand and a life expectancy of about 50 years.

In developed countries in Europe and Asia, the life expectancy across those nations is higher than African continent because of some likely factor like health diet, clean water supply, low rate of violence, less poverty, high medical care, good exercise, careful planning, among others contributed to their lengthy life span. Countries in Asia and Europe hold many of the top rank in the list of the world 15 healthiest countries with an average life span of between 80-84 years. Australia (81.9), Hong Kong (82.12), Andorra (82.5), Singapore (83.75), San Marino (83.07), Japan (83.91), Italy (81.86); (WFB, 2011). Porter (1996) employed a Solow-Swan economic growth model with exogenous saving rate to determine the relationship between population growth and economic growth. The model assumed that both the saving rate and the consumption rate are given. Assuming, a household owns the input and manages the technology. The production technology is assumed to take the form

Y = f (K, L), (1)

Where Y is total output,
K is total physical capital,
And L is the size of the labour input

The production function exhibits positive and diminishing marginal products with respect to each input and also exhibits constant returns to scale. The economy is assumed to be a one-sector economy, where output can be either consumed or invested and capital depreciates at a constant positive rate (δ). The growth rate of population is exogenous. The model further assumes that this growth rate is a constant (n) and that labour supply per person is given. Normalizing the population size at time zero and the work intensity to one yield the following is the labour input

L = en (2)

The net increase in per capita capital is:

k= sf (k) − (n + δ) k (3)

The first term on the right-hand side (RHS) is saving per capita out of output per capita and the second term is the effective depreciation per capita. Defining a steady state as a situation in which the quantities, such as capital, population, and output, grow at constant rates. In the Solow-Swan model, a steady state exists if the net increase in per capita capital is equal to zero. Denoting steady state values with an asterisk the steady state values are given by:

sf (k*) = (n + δ)k*, y* = f (k*) and c* = (1 − s)f(k*). (4)

Since the per capita values are constant in steady state, the levels of total output, total consumption, and total capital must grow at the same rate, which is the same as that of population growth (n). An increase in the rate of population growth in steady state does not affect the growth rate of the per capita variables, since these rates are equal to zero in steady state. However, an increase in fertility does lead to a decrease in the level of capital per capita and therefore to a decrease in output and consumption per capita. This is the capital dilution effect. An increase in the population growth rate leads to a decline in the growth rate of the per capita variables. For model with exogenous saving rates, higher population growth leads to lower standard of living per capita measured either as consumption or in growth of consumption.

Becker and Hoover (1998) develops altruistic models of intergenerational transfers where the behaviour of individuals is guided by a utility function that is increasing in own consumption and the utility achieved by one’s offspring. The utility of the offspring depends, in turn, on their own consumption and the utility of their offspring. Through this inter-linking chain, the current generation consumes and transfers resources to its children influenced by its concern not only for its own children but for all future generations. An important implication of this model is that familial transfers will neutralize fiscal policy. When a government exercises expansionary fiscal policy, it stimulates the economy by increasing current spending financed by issuing debt. From the perspective of intergenerational transfers, the policy is an effort to stimulate spending by transferring resources to current generations from future generations. According to this model however, the public policy is undone by altruistic households. They compensate future generations by increasing their saving and accumulating wealth, exactly offsetting the increase in public debt. This model implies that public intergenerational transfers and private intergenerational transfers are perfect substitutes. A change in public transfers is matched dollar for dollar by a compensating change in private transfers.

3.METHODOLOGY

3.1. Introduction

This describes theoretical model, empirical model and the research design. The research design reveals the type of data and method of data collection.

3.2. Theoretical Framework

In this work, the following were postulated.

That x and y are directly proportional, where y is the life expectancy and x is the population growth rate, y α x

That, they are related by the function, y = αxβ. ε (5)

Where y = ln y, β is the parameter and ε is the random error.

This gave rise to the non-linear model which can be made intrinsically linear using the log-log transformation. Following the log-log transformation, there was a form regression model which can be estimated using the ordinary least square (OLS).

3.3. Regression Analysis

3.3.1. Introduction

3.32. Error and Hypothesis Testing

For model in (7) above, given the parameter in (12) and (13), we have the sum squared error

3.4. Correlation Analysis

3.5. Data Type and Source

This study makes use of published data of the United Nations, Department of Economic and Social Affairs, Population Division in 2013.

4. ANALYSIS AND ESTIMATION RESULTS

4.1. Introduction

The analysis of this work was done by the use of Statistical Package for Social Sciences (SPSS 19). Firstly, Global Life Expectancy at Birth of 70.01 years was considered in order to identify countries that fall below the world standard of 70.01. Out of 194 countries of the world considered, only 74 countries (about 38.1%) met the world standard of 70.01. African countries were the country that mostly fell below the world standard.Similarly, the study classified the population growth rate in terms of log (Growth) and with log growth < 1 and log growth ≥ 1 were segregated. Moreover, the study showed that about 123 countries (63.4%) had growth rate.

Table-1. The population growth rate and life expectancy of countries across seven continent, 2013.

Country

Population

growth

life expectancy

Log of

Log of Life

Class

Global

mid-2013

rate

Growth Rate

Expectancy

Standard

Afghanistan

30,551,674

2.39

60.75

0.3784

1.7835

FALSE

TRUE

FALSE

TRUE

Albania

3,173,271

0.3

77.29

-0.5229

1.8881

TRUE

FALSE

TRUE

FALSE

Algeria

39,208,194

1.84

70.93

0.2648

1.8508

FALSE

FALSE

FALSE

FALSE

Angola

21,471,618

3.09

51.68

0.49

1.7133

FALSE

TRUE

FALSE

TRUE

Antigua and Barbuda

89,985

1.03

75.87

0.0128

1.8801

FALSE

FALSE

FALSE

FALSE

Argentina

41,446,246

0.86

76.21

-0.0655

1.882

TRUE

FALSE

TRUE

FALSE

Armenia

2,976,566

0.18

74.47

-0.7447

1.872

TRUE

FALSE

TRUE

FALSE

Aruba

102,911

0.45

75.39

-0.3468

1.8773

TRUE

FALSE

TRUE

FALSE

Australia

23,342,553

1.31

82.4

0.1173

1.9159

FALSE

FALSE

FALSE

FALSE

Austria

8,495,145

0.37

81.05

-0.4318

1.9088

TRUE

FALSE

TRUE

FALSE

Azerbaijan

9,413,420

1.11

70.64

0.0453

1.8491

FALSE

FALSE

FALSE

FALSE

Bahamas

377,374

1.45

75.15

0.1614

1.8759

FALSE

FALSE

FALSE

FALSE

Bahrain

1,332,171

1.66

76.53

0.2201

1.8838

FALSE

FALSE

FALSE

FALSE

Bangladesh

156,594,962

1.19

70.46

0.0755

1.8479

FALSE

FALSE

FALSE

FALSE

Barbados

284,644

0.5

75.29

-0.301

1.8767

TRUE

FALSE

TRUE

FALSE

Belgium

11,104,476

0.44

80.45

-0.3565

1.9055

TRUE

FALSE

TRUE

FALSE

Belize

331,900

2.38

73.78

0.3766

1.8679

FALSE

FALSE

FALSE

FALSE

Benin

10,323,474

2.69

59.2

0.4298

1.7723

FALSE

TRUE

FALSE

TRUE

Bhutan

753,947

1.6

68.04

0.2041

1.8328

FALSE

TRUE

FALSE

TRUE

Bolivia (Plurinational State of)

10,671,200

1.64

67.11

0.2148

1.8268

FALSE

TRUE

FALSE

TRUE

Botswana

2,021,144

0.87

47.41

-0.0605

1.6759

TRUE

TRUE

TRUE

TRUE

Brazil

200,361,925

0.85

73.8

-0.0706

1.8681

TRUE

FALSE

TRUE

FALSE

Brunei Darussalam

417,784

1.35

78.45

0.1303

1.8946

FALSE

FALSE

FALSE

FALSE

Burkina Faso

16,934,839

2.84

56.14

0.4533

1.7493

FALSE

TRUE

FALSE

TRUE

Burundi

10,162,532

3.16

53.9

0.4997

1.7316

FALSE

TRUE

FALSE

TRUE

Cambodia

15,135,169

1.75

71.63

0.243

1.8551

FALSE

FALSE

FALSE

FALSE

Cameroon

22,253,959

2.52

54.88

0.4014

1.7394

FALSE

TRUE

FALSE

TRUE

Canada

35,181,704

1

81.41

0

1.9107

FALSE

FALSE

FALSE

FALSE

Cape Verde

498,897

0.83

74.92

-0.0809

1.8746

TRUE

FALSE

TRUE

FALSE

Central African Republic

4,616,417

1.98

49.93

0.2967

1.6984

FALSE

TRUE

FALSE

TRUE

Chad

12,825,314

2.98

50.98

0.4742

1.7074

FALSE

TRUE

FALSE

TRUE

Channel Islands

162,018

0.5

80.23

-0.301

1.9043

TRUE

FALSE

TRUE

FALSE

Chile

17,619,708

0.88

79.85

-0.0555

1.9023

TRUE

FALSE

TRUE

FALSE

China

1,385,566,537

0.61

75.25

-0.2147

1.8765

TRUE

FALSE

TRUE

FALSE

China, Hong Kong SAR

7,203,836

0.74

83.28

-0.1308

1.9205

TRUE

FALSE

TRUE

FALSE

China, Macao SAR

566,375

1.78

80.29

0.2504

1.9047

FALSE

FALSE

FALSE

FALSE

Colombia

48,321,405

1.29

73.93

0.1106

1.8688

FALSE

FALSE

FALSE

FALSE

Comoros

734,917

2.4

60.77

0.3802

1.7837

FALSE

TRUE

FALSE

TRUE

Congo

4,447,632

2.55

58.63

0.4065

1.7681

FALSE

TRUE

FALSE

TRUE

Congo, Democratic Republic of the

67,513,677

2.72

49.84

0.4346

1.6976

FALSE

TRUE

FALSE

TRUE

Costa Rica

4,872,166

1.37

79.83

0.1367

1.9022

FALSE

FALSE

FALSE

FALSE

Curaçao

158,760

2.17

77.04

0.3365

1.8867

FALSE

FALSE

FALSE

FALSE

Cyprus

1,141,166

1.08

79.76

0.0334

1.9018

FALSE

FALSE

FALSE

FALSE

Czech Republic

10,702,197

0.42

77.59

-0.3768

1.8898

TRUE

FALSE

TRUE

FALSE

Côte d'Ivoire

20,316,086

2.31

50.51

0.3636

1.7034

FALSE

TRUE

FALSE

TRUE

Denmark

5,619,096

0.4

79.29

-0.3979

1.8992

TRUE

FALSE

TRUE

FALSE

Djibouti

872,932

1.52

61.62

0.1818

1.7897

FALSE

TRUE

FALSE

TRUE

Dominican Republic

10,403,761

1.23

73.29

0.0899

1.865

FALSE

FALSE

FALSE

FALSE

Ecuador

15,737,878

1.57

76.36

0.1959

1.8829

FALSE

FALSE

FALSE

FALSE

Egypt

82,056,378

1.63

71.06

0.2122

1.8516

FALSE

FALSE

FALSE

FALSE

El Salvador

6,340,454

0.66

72.49

-0.1805

1.8603

TRUE

FALSE

TRUE

FALSE

Equatorial Guinea

757,014

2.77

52.88

0.4425

1.7233

FALSE

TRUE

FALSE

TRUE

Eritrea

6,333,135

3.2

62.59

0.5051

1.7965

FALSE

TRUE

FALSE

TRUE

Ethiopia

94,100,756

2.55

63.32

0.4065

1.8015

FALSE

TRUE

FALSE

TRUE

Fiji

881,065

0.73

69.72

-0.1367

1.8434

TRUE

TRUE

TRUE

TRUE

Finland

5,426,323

0.34

80.45

-0.4685

1.9055

TRUE

FALSE

TRUE

FALSE

France

64,291,280

0.55

81.71

-0.2596

1.9123

TRUE

FALSE

TRUE

FALSE

French Guiana

249,227

2.48

77.02

0.3945

1.8866

FALSE

FALSE

FALSE

FALSE

French Polynesia

276,831

1.07

76.12

0.0294

1.8815

FALSE

FALSE

FALSE

FALSE

Gabon

1,671,711

2.36

63.31

0.3729

1.8015

FALSE

TRUE

FALSE

TRUE

Gambia

1,849,285

3.18

58.7

0.5024

1.7686

FALSE

TRUE

FALSE

TRUE

Ghana

25,904,598

2.13

60.99

0.3284

1.7853

FALSE

TRUE

FALSE

TRUE

Greece

11,127,990

0.03

80.69

-1.5229

1.9068

TRUE

FALSE

TRUE

FALSE

Grenada

105,897

0.38

72.69

-0.4202

1.8615

TRUE

FALSE

TRUE

FALSE

Guadeloupe

465,800

0.5

80.84

-0.301

1.9076

TRUE

FALSE

TRUE

FALSE

Guam

165,124

1.27

78.71

0.1038

1.896

FALSE

FALSE

FALSE

FALSE

Guatemala

15,468,203

2.51

71.96

0.3997

1.8571

FALSE

FALSE

FALSE

FALSE

Guinea

11,745,189

2.54

55.92

0.4048

1.7476

FALSE

TRUE

FALSE

TRUE

Guinea-Bissau

1,704,255

2.39

54.17

0.3784

1.7338

FALSE

TRUE

FALSE

TRUE

Guyana

799,613

0.54

66.2

-0.2676

1.8209

TRUE

TRUE

TRUE

TRUE

Haiti

10,317,461

1.38

62.96

0.1399

1.7991

FALSE

TRUE

FALSE

TRUE

Honduras

8,097,688

2

73.7

0.301

1.8675

FALSE

FALSE

FALSE

FALSE

Iceland

329,535

1.14

82.01

0.0569

1.9139

FALSE

FALSE

FALSE

FALSE

India

1,252,139,596

1.24

66.28

0.0934

1.8214

FALSE

TRUE

FALSE

TRUE

Indonesia

249,865,631

1.21

70.72

0.0828

1.8495

FALSE

FALSE

FALSE

FALSE

Iran (Islamic Republic of)

77,447,168

1.3

73.9

0.1139

1.8686

FALSE

FALSE

FALSE

FALSE

Iraq

33,765,232

2.89

69.43

0.4609

1.8415

FALSE

TRUE

FALSE

TRUE

Ireland

4,627,173

1.13

80.58

0.0531

1.9062

FALSE

FALSE

FALSE

FALSE

Israel

7,733,144

1.3

81.72

0.1139

1.9123

FALSE

FALSE

FALSE

FALSE

Italy

60,990,277

0.21

82.29

-0.6778

1.9153

TRUE

FALSE

TRUE

FALSE

Jamaica

2,783,888

0.52

73.45

-0.284

1.866

TRUE

FALSE

TRUE

FALSE

Jordan

7,273,799

3.5

73.78

0.5441

1.8679

FALSE

FALSE

FALSE

FALSE

Kazakhstan

16,440,586

1.04

66.44

0.017

1.8224

FALSE

TRUE

FALSE

TRUE

Kenya

44,353,691

2.67

61.56

0.4265

1.7893

FALSE

TRUE

FALSE

TRUE

Kiribati

102,351

1.54

68.75

0.1875

1.8373

FALSE

TRUE

FALSE

TRUE

Korea, Dem. People's Republic of

24,895,480

0.53

69.9

-0.2757

1.8445

TRUE

TRUE

TRUE

TRUE

Korea, Republic of

49,262,698

0.53

81.37

-0.2757

1.9105

TRUE

FALSE

TRUE

FALSE

Kuwait

3,368,572

3.61

74.24

0.5575

1.8706

FALSE

FALSE

FALSE

FALSE

Kyrgyzstan

5,547,548

1.35

67.48

0.1303

1.8292

FALSE

TRUE

FALSE

TRUE

Lao People's Democratic Republic

6,769,727

1.86

68.08

0.2695

1.833

FALSE

TRUE

FALSE

TRUE

Lebanon

4,821,971

3.04

79.81

0.4829

1.9021

FALSE

FALSE

FALSE

FALSE

Lesotho

2,074,465

1.08

49.5

0.0334

1.6946

FALSE

TRUE

FALSE

TRUE

Liberia

4,294,077

2.58

60.25

0.4116

1.78

FALSE

TRUE

FALSE

TRUE

Libya

6,201,521

0.9

75.21

-0.0458

1.8763

TRUE

FALSE

TRUE

FALSE

Luxembourg

530,380

1.35

80.45

0.1303

1.9055

FALSE

FALSE

FALSE

FALSE

Macedonia

2,107,158

0.07

75.13

-1.1549

1.8758

TRUE

FALSE

TRUE

FALSE

Madagascar

22,924,851

2.79

64.51

0.4456

1.8096

FALSE

TRUE

FALSE

TRUE

Malawi

16,362,567

2.85

55.1

0.4548

1.7412

FALSE

TRUE

FALSE

TRUE

Malaysia

29,716,965

1.61

74.93

0.2068

1.8747

FALSE

FALSE

FALSE

FALSE

Maldives

345,023

1.89

77.68

0.2765

1.8903

FALSE

FALSE

FALSE

FALSE

Mali

15,301,650

3.01

54.82

0.4786

1.7389

FALSE

TRUE

FALSE

TRUE

Malta

429,004

0.3

79.66

-0.5229

1.9012

TRUE

FALSE

TRUE

FALSE

Martinique

403,682

0.24

81.3

-0.6198

1.9101

TRUE

FALSE

TRUE

FALSE

Mauritania

3,889,880

2.45

61.48

0.3892

1.7887

FALSE

TRUE

FALSE

TRUE

Mauritius

1,244,403

0.37

73.54

-0.4318

1.8665

TRUE

FALSE

TRUE

FALSE

Mayotte

222,152

2.71

79.05

0.433

1.8979

FALSE

FALSE

FALSE

FALSE

Mexico

122,332,399

1.21

77.38

0.0828

1.8886

FALSE

FALSE

FALSE

FALSE

Micronesia (Fed. States of)

103,549

0.16

68.93

-0.7959

1.8384

TRUE

TRUE

TRUE

TRUE

Mongolia

2,839,073

1.49

67.36

0.1732

1.8284

FALSE

TRUE

FALSE

TRUE

Montenegro

621,383

0.05

74.76

-1.301

1.8737

TRUE

FALSE

TRUE

FALSE

Morocco

33,008,150

1.41

70.84

0.1492

1.8503

FALSE

FALSE

FALSE

FALSE

Mozambique

25,833,752

2.47

50.2

0.3927

1.7007

FALSE

TRUE

FALSE

TRUE

Myanmar

53,259,018

0.84

65.08

-0.0757

1.8134

TRUE

TRUE

TRUE

TRUE

Namibia

2,303,315

1.87

64.34

0.2718

1.8085

FALSE

TRUE

FALSE

TRUE

Nepal

27,797,457

1.15

68.19

0.0607

1.8337

FALSE

TRUE

FALSE

TRUE

Netherlands

16,759,229

0.27

80.94

-0.5686

1.9082

TRUE

FALSE

TRUE

FALSE

New Caledonia

256,496

1.32

76.19

0.1206

1.8819

FALSE

FALSE

FALSE

FALSE

New Zealand

4,505,761

1.02

81.04

0.0086

1.9087

FALSE

FALSE

FALSE

FALSE

Nicaragua

6,080,478

1.44

74.67

0.1584

1.8731

FALSE

FALSE

FALSE

FALSE

Niger

17,831,270

3.85

58.14

0.5855

1.7645

FALSE

TRUE

FALSE

TRUE

Nigeria

173,615,345

2.78

52.29

0.444

1.7184

FALSE

TRUE

FALSE

TRUE

Norway

5,042,671

1

81.42

0

1.9107

FALSE

FALSE

FALSE

FALSE

Oman

3,632,444

7.89

76.43

0.8971

1.8833

FALSE

FALSE

FALSE

FALSE

Pakistan

182,142,594

1.66

66.48

0.2201

1.8227

FALSE

TRUE

FALSE

TRUE

Palestine, State of

4,326,295

2.51

73.12

0.3997

1.864

FALSE

FALSE

FALSE

FALSE

Panama

3,864,170

1.62

77.46

0.2095

1.8891

FALSE

FALSE

FALSE

FALSE

Papua New Guinea

7,321,262

2.14

62.31

0.3304

1.7946

FALSE

TRUE

FALSE

TRUE

Paraguay

6,802,295

1.7

72.2

0.2304

1.8585

FALSE

FALSE

FALSE

FALSE

Peru

30,375,603

1.26

74.68

0.1004

1.8732

FALSE

FALSE

FALSE

FALSE

Philippines

98,393,574

1.71

68.63

0.233

1.8365

FALSE

TRUE

FALSE

TRUE

Poland

38,216,635

0.01

76.32

-2

1.8826

TRUE

FALSE

TRUE

FALSE

Portugal

10,608,156

0.04

79.83

-1.3979

1.9022

TRUE

FALSE

TRUE

FALSE

Qatar

2,168,673

5.9

78.3

0.7709

1.8938

FALSE

FALSE

FALSE

FALSE

Rwanda

11,776,522

2.74

63.62

0.4378

1.8036

FALSE

TRUE

FALSE

TRUE

Reunion

875,375

1.16

79.52

0.0645

1.9005

FALSE

FALSE

FALSE

FALSE

Saint Lucia

182,273

0.83

74.69

-0.0809

1.8733

TRUE

FALSE

TRUE

FALSE

Saint Vincent and the Grenadines

109,373

0.01

72.41

-2

1.8598

TRUE

FALSE

TRUE

FALSE

Samoa

190,372

0.76

73.01

-0.1192

1.8634

TRUE

FALSE

TRUE

FALSE

Sao Tome & Principe

192,993

2.58

66.24

0.4116

1.8211

FALSE

TRUE

FALSE

TRUE

Saudi Arabia

28,828,870

1.85

75.37

0.2672

1.8772

FALSE

FALSE

FALSE

FALSE

Senegal

14,133,280

2.9

63.28

0.4624

1.8013

FALSE

TRUE

FALSE

TRUE

Seychelles

92,838

0.55

73.12

-0.2596

1.864

TRUE

FALSE

TRUE

FALSE

Sierra Leone

6,092,075

1.88

45.34

0.2742

1.6565

FALSE

TRUE

FALSE

TRUE

Singapore

5,411,737

2.02

82.2

0.3054

1.9149

FALSE

FALSE

FALSE

FALSE

Slovakia

5,450,223

0.09

75.32

-1.0458

1.8769

TRUE

FALSE

TRUE

FALSE

Slovenia

2,071,997

0.24

79.47

-0.6198

1.9002

TRUE

FALSE

TRUE

FALSE

Solomon Islands

561,231

2.09

67.53

0.3201

1.8295

FALSE

TRUE

FALSE

TRUE

Somalia

10,495,583

2.87

54.88

0.4579

1.7394

FALSE

TRUE

FALSE

TRUE

South Africa

52,776,130

0.78

57.11

-0.1079

1.7567

TRUE

TRUE

TRUE

TRUE

South Sudan

11,296,173

4.02

54.97

0.6042

1.7401

FALSE

TRUE

FALSE

TRUE

Spain

46,926,963

0.44

82

-0.3565

1.9138

TRUE

FALSE

TRUE

FALSE

Sri Lanka

21,273,228

0.81

74.23

-0.0915

1.8706

TRUE

FALSE

TRUE

FALSE

Sudan

37,964,306

2.11

61.92

0.3243

1.7918

FALSE

TRUE

FALSE

TRUE

Suriname

539,276

0.88

70.9

-0.0555

1.8506

TRUE

FALSE

TRUE

FALSE

Swaziland

1,249,514

1.49

49.19

0.1732

1.6919

FALSE

TRUE

FALSE

TRUE

Sweden

9,571,105

0.65

81.74

-0.1871

1.9124

TRUE

FALSE

TRUE

FALSE

Switzerland

8,077,833

1.02

82.51

0.0086

1.9165

FALSE

FALSE

FALSE

FALSE

Syrian Arab Republic

21,898,061

0.67

74.37

-0.1739

1.8714

TRUE

FALSE

TRUE

FALSE

Taiwan

23,329,772

0.24

79.26

-0.6198

1.8991

TRUE

FALSE

TRUE

FALSE

Tajikistan

8,207,834

2.43

67.14

0.3856

1.827

FALSE

TRUE

FALSE

TRUE

Tanzania, United Republic of

49,253,126

3.02

61.36

0.48

1.7879

FALSE

TRUE

FALSE

TRUE

Thailand

67,010,502

0.3

74.27

-0.5229

1.8708

TRUE

FALSE

TRUE

FALSE

Timor-Leste

1,132,879

1.66

67.3

0.2201

1.828

FALSE

TRUE

FALSE

TRUE

Togo

6,816,982

2.57

56.41

0.4099

1.7514

FALSE

TRUE

FALSE

TRUE

Tonga

105,323

0.43

72.59

-0.3665

1.8609

TRUE

FALSE

TRUE

FALSE

Trinidad and Tobago

1,341,151

0.28

69.81

-0.5528

1.8439

TRUE

TRUE

TRUE

TRUE

Tunisia

10,996,515

1.1

75.77

0.0414

1.8795

FALSE

FALSE

FALSE

FALSE

Turkey

74,932,641

1.22

75.09

0.0864

1.8756

FALSE

FALSE

FALSE

FALSE

Turkmenistan

5,240,072

1.27

65.39

0.1038

1.8155

FALSE

TRUE

FALSE

TRUE

Uganda

37,578,876

3.33

59.02

0.5224

1.771

FALSE

TRUE

FALSE

TRUE

United Arab Emirates

9,346,129

2.52

76.75

0.4014

1.8851

FALSE

FALSE

FALSE

FALSE

United Kingdom

63,136,265

0.57

80.45

-0.2441

1.9055

TRUE

FALSE

TRUE

FALSE

United States of America

320,050,716

0.81

78.86

-0.0915

1.8969

TRUE

FALSE

TRUE

FALSE

United States Virgin Islands

106,627

0.1

80.05

-1

1.9034

TRUE

FALSE

TRUE

FALSE

Uruguay

3,407,062

0.34

77.14

-0.4685

1.8873

TRUE

FALSE

TRUE

FALSE

Uzbekistan

28,934,102

1.35

68.19

0.1303

1.8337

FALSE

TRUE

FALSE

TRUE

Vanuatu

252,763

2.21

71.48

0.3444

1.8542

FALSE

FALSE

FALSE

FALSE

Venezuela (Bolivarian Republic of)

30,405,207

1.49

74.55

0.1732

1.8724

FALSE

FALSE

FALSE

FALSE

Viet Nam

91,679,733

0.95

75.87

-0.0223

1.8801

TRUE

FALSE

TRUE

FALSE

Western Sahara

567,315

3.21

67.61

0.5065

1.83

FALSE

TRUE

FALSE

TRUE

Yemen

24,407,381

2.3

63.02

0.3617

1.7995

FALSE

TRUE

FALSE

TRUE

Zambia

14,538,640

3.21

57.66

0.5065

1.7609

FALSE

TRUE

FALSE

TRUE

Zimbabwe

14,149,648

2.81

59.84

0.4487

1.777

FALSE

TRUE

FALSE

TRUE

-0.48319

-0.44939

Source: Department of Economic and Social Affairs, United Nations, 2013.

4.2. Scatter Diagram

Scatter diagram for life expectancy against population growth rate and log of life expectancy against log of population growth rate were considered to see the behaviour of the countries under consideration. The study discovered that the behaviour of both diagrams were the same, but the negative slope in both diagrams implied that, population growth increased ,then the life expectancy at birth decreased. See Fig. 1 below.

Fig-1. Scatter Diagram of Life Expectancy against Growth Rate Source: Department of Economic and Social Affairs, United Nations, 2013.

4.3. Regression Analysis

The Estimated Regression model is of the form

Log growth rate = 6.20-3.34 log life expectancy

This Rate of growth given by β* = -3.3411 indicated a negative rate of change which also suggest that as log growth rate increases, log life expectancy decreases. Consequently, life expectancy decreases with population growth. This is further collaborated by the hypotheses

Ho: β1= 0
H1 :β1=0

Where Hois reported, the negative effect on life expectancy is very significant.

4.4. Regression Analysis: log of Population Growth Rate Versus Log of Life Expectancy

The correlation analysis of the study showed that when comparing the relationship between life expectancy and population growth rate, there is a negative relationship

(-0.4832) between the two variables satisfying the postulation that increase in growth rate decrease life expectancy.

5. SUMMARY

5.1. Introduction

This aspect summarizes the study and makes conclusion based on the result. The policy implications from the findings are also presented.

5.2. Summary

The relationship between the life expectancy and the population growth rate has therefore been fundamental to the policy makers in different countries of the world. However, there has been no consensus whether population growth is beneficial or detrimental to the life expectancy since the relationship of the two varies among countries. But, the study can summarily established that while the population growth rate increases then the life expectancy tends to decrease and vice versa through the use regression and correlation approach.

5.3. Conclusion

Conclusively, the finding of the study supported the first stage of demographic transition called pre-Malthusian regime, which predicts the relationship between the population’s growth rate and life expectancy to remain parallel since the increase in one leads to decrease in the other.

5.4. Recommendations

In view of the findings that life expectancy will increase, if the population growth rate decreases and vice-versa. Therefore, life expectancy will definitely decrease in view of the fact that the continuous practice of raising large family will affect life expectancy on raising large family involve a lot of stress in providing necessary benefit for their up keeping, feeding, clothing, provision of better health facilities, education and other care, which bring along stress, agitation especially in paying bills for education, health, feeding, and clothing among others. Health wise, the stress and related cause will affect the life expectancy.

It is hereby recommended that:

The citizen especially African should be encouraged desisting from raising large family.

The introduction of a legislature improving sanction on whoever raises large family as it is practiced in China and Indian.

Introduction of preventive measures during sexual relationship to curb unwanted pregnancies by the government.

Enforcing and introducing abortion or other measure to curb raising large family, though the government has to have a political will as many religious bodies will definitely kick against the policy.

Proper orientation should be given to would – be newly wedded couples, married and singles about the benefits and disadvantages of not raising large family.

Hospital should be used as a measure to advise or even sanction any family that go against national figure of the family.

Pregnant mother should be enlightened on the benefits of raising small family during ante– natal clinics.

REFERENCES

Becker, A.J. and E.M. Hoover, 1998. Population growth and economic development in low- income countries. Princeton: Princeton University Press. pp: 610-619.

Bhargava, A., 2003. Population growth in a model of economic growth with human capital accumulation and horizontal R &D. Milan: University of Milan. pp: 510-517.

Charkraborty and Idrani, 2010. Capital structure in an emerging stock market. A case of India. Research in International Business and Finance, 24(3): 295-314. View at Google Scholar | View at Publisher

CIA World Fact Books, 2011. 12(1): 89-95.

Malthus, T.R., 1998. An essay on the principles of population. Cambridge: Cambridge University Press. pp: 121-131.